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A Survey of Multiple Pedestrian tracking Based on tracking-by-detection Framework

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第5期31卷 1819-1833页

作者： Sun, Zhihong Chen, Jun Chao, Liang Ruan, Weijian Mukherjee, Mithun Wuhan Univ Sch Comp Sci Natl Engn Res Ctr Multimedia Software Wuhan 430072 Peoples R China Wuhan Univ Hubei Key Lab Multimedia & Network Commun Engn Wuhan 430072 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, tracking-by-detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed.

关键词： Task analysis Noise measurement Trajectory Video sequences Machine learning Visualization Radar tracking Computer vision multiple pedestrian tracking tracking-by-detection data association

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Decode-MOT: How Can We Hurdle Frames to Go Beyond tracking-by-detection?

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 4378-4392页

作者： Lee, Seong-Ho Park, Dae-Hyeon Bae, Seung-Hwan Inha Univ Vis & Learning Lab Incheon 22212 South Korea SK Hynix Icheon 17336 Gyeonggi Do South Korea

The speed of tracking-by-detection (TBD) greatly depends on the number of running a detector because the detection is the most expensive operation in TBD. In many practical cases, multi-object tracking (MOT) can be, however, achieved based tracking-by-motion (TBM) only. This is a possible solution without much loss of MOT accuracy when the variations of object cardinality and motions are not much within consecutive frames. Therefore, the MOT problem can be transformed to find the best TBD and TBM mechanism. To achieve it, we propose a novel decision coordinator for MOT (Decode-MOT) which can determine the best TBD/TBM mechanism according to scene and tracking contexts. In specific, our Decode-MOT learns tracking and scene contextual similarities between frames. Because the contextual similarities can vary significantly according to the used trackers and tracking scenes, we learn the Decode-MOT via self-supervision. The evaluation results on MOT challenge datasets prove that our method can boost the tracking speed greatly while keeping the state-of-the-art MOT accuracy. Our code will be available at https://***/reussite-cv/Decode-MOT.

关键词： Multi-object tracking tracking-by-detection tracking-by-motion scene and tracking contextual learning hierarchical association

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Extended Feature Descriptor and Vehicle Motion Model with tracking-by-detection for Pedestrian Active Safety

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第2期E97D卷 296-304页

作者： Kataoka, Hirokatsu Tamura, Kimimasa Iwata, Kenji Satoh, Yutaka Matsui, Yasuhiro Aoki, Yoshimitsu Keio Univ Yokohama Kanagawa 2238522 Japan Japan Soc Promot Sci Tokyo 1020083 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058568 Japan Natl Traff Safety & Environm Lab Chofu Tokyo 1820012 Japan

The percentage of pedestrian deaths in traffic accidents is on the rise in Japan. In recent years, there have been calls for measures to be introduced to protect vulnerable road users such as pedestrians and cyclists. In this study, a method to detect and track pedestrians using an in-vehicle camera is presented. We improve the technology of detecting pedestrians by using the highly accurate images obtained with a monocular camera. In the detection step, we employ ECoHOG as the feature descriptor;it accumulates the integrated gradient intensities. In the tracking step, we apply an effective motion model using optical flow and the proposed feature descriptor ECoHOG in a tracking-by-detection framework. These techniques were verified using images captured on real roads.

关键词： pedestrian active safety tracking-by-detection ECoHOG particle filter vehicle motion model

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Comparative Evaluations of Selected tracking-by-detection Approaches

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第4期29卷 996-1010页

作者： Mekonnen, Alhayat Ali Lerasle, Frederic CNRS LAAS F-31400 Toulouse France Univ Toulouse UPS LAAS F-31400 Toulouse France

In this paper, we present a comparative evaluation of various multi-person tracking-by-detection approaches on public data sets. This paper investigates five popular trackers coupled with six relevant visual people detectors evaluated on seven public data sets. The evaluation emphasizes on exhibited performance variation depending on tracker-detector choices. Our experimental results show that the overall performance depends on how challenging the data set is, the performance of the detector on the specific data set, and the tracker-detector combination. Some trackers are more sensitive to the choice of a detector and some detectors to the choice of a tracker than others. Based on our results, two of the trackers demonstrate the best performances consistently across different data sets, whereas the best performing detectors vary per data set. This underscores the need for careful application context specific evaluation when choosing a detector.

关键词： tracking-by-detection multi-person tracking people detection

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Online Multiperson tracking-by-detection from a Single, Uncalibrated Camera

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2011年第9期33卷 1820-1833页

作者： Breitenstein, Michael D. Reichlin, Fabian Leibe, Bastian Koller-Meier, Esther Van Gool, Luc ETH Comp Vis Lab Zurich Switzerland Rhein Westfal TH Aachen UMIC Res Ctr Mobile Multimedia Proc Grp Aachen Germany Katholieke Univ Leuven ESAT PSI IBBT Louvain Belgium

In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.

关键词： Multi-object tracking tracking-by-detection detector confidence particle filter pedestrian detection particle filtering sequential Monte Carlo estimation online learning detector confidence surveillance sports analysis traffic safety

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Hierarchical Grid-based Multi-People tracking-by-detection With Global Optimization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第11期24卷 4197-4212页

作者： Chen, Lili Wang, Wei Panin, Giorgio Knoll, Alois Tech Univ Munich Dept Informat D-85748 Garching Germany German Aerosp Ctr Inst Robot & Mechatron D-82230 Wessling Germany

We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.

关键词： tracking-by-detection hierarchical grid-based detection orientation estimation global optimal data association

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Motion estimation and multi-stage association for tracking-by-detection

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COMPLEX & INTELLIGENT SYSTEMS 2024年第2期10卷 2445-2458页

作者： Li, Ye Wu, Lei Chen, Yiping Wang, Xinzhong Yin, Guangqiang Wang, Zhiguo Shenzhen Inst Informat Technol Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Kashi Inst Elect & Informat Ind Kashi Peoples R China

Multi-object tracking (MOT) aims to locate and identify objects in videos. As deep learning brings excellent performances to object detection, the tracking-by-detection (TBD) has gradually become a mainstream tracking framework. However, some drawbacks still exist in the current TBD framework: (1) inaccurate prediction of the bounding boxes would occur in the detection part, which is caused by overlooking the actual pedestrian ratio in the surveillance scene. (2) The width of the bounding boxes in the next frame might be indirectly predicted by the aspect ratio, which increases the error of width prediction in the motion prediction part. (3) Association is only performed for high-confidence detection boxes, and the low-confidence boxes caused by occlusion are discarded in the data association part, resulting in fragmentation of trajectories. To address the above issues, we propose a multi-target tracking model incorporating motion estimation and multi-stage association (MEMA). First, the aspect ratio of the ground-true bounding box is introduced to improve the fit of the detection and the ground-true bounding box, and we design the elliptical Gaussian kernel to improve the positioning accuracy of the object center point. Then, the prediction state vector of the Kalman filter is modified to predict the width and its corresponding velocity directly. It can reduce the width error of the prediction box and eliminate the velocity error of the motion estimation, which leads to a more pedestrian-friendly prediction bounding box. Finally, we propose a multi-stage association strategy to correlate different confidence boxes. Without using the appearance feature, the strategy can reduce the impact of occlusion and improve the tracking performance. On the MOT17 test set, the method proposed in this paper achieves a MOTA of 74.3% and an IDF1 of 72.4%, outperforming the current SOTA.

关键词： Multi-object tracking (MOT) tracking-by-detection Motion estimation Multi-stage association

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Real-time tracking-by-detection Framework for Traffic Applications via Deep Learning based Convolutional Neural Network

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JOURNAL OF ELECTRICAL SYSTEMS 2020年第3期16卷 381-392页

作者： Madah-Ul-Mustafa Yu, Zhu Liang South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

Vision-based target tracking is one of the core parts of intelligent traffic video surveillance systems due to its assistance in reducing risks of traffic accidents and traffic jams on roads. This paper proposes a new tracking-by-detection method in the domain of traffic applications by using the powerful ability of deep learning based object detection technique into vision-based tracking for vehicles. The proposed method uses transfer learning technique to achieve state-of-the-art tracking performance but building upon a powerful object detector while only requiring few hundreds of images data for training. The experimental results not only validates the performance of the proposed transfer learning technique and also shows that tracking can be achieved using this approach. Furthermore, qualitative and quantitative results on challenging dataset show that the proposed tracking method achieves competitive performance with the state-of-the-art methods.

关键词： Vision-based tracking Deep learning CNN's tracking-by-detection

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A tracking-by-detection System for Pedestrian tracking Using Deep Learning Technique and Color Information

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2019年第4期15卷 1017-1028页

作者： Mai Thanh Nhat Truong Kim, Sanghoon Hankyong Natl Univ Dept Elect Elect & Control Engn Anseong South Korea

Pedestrian tracking is a particular object tracking problem and an important component in various vision-based applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.

关键词： Color Distribution Convolutional Neural Network Pedestrian tracking tracking-by-detection

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Lightweight tracking-by-detection system for multiple pedestrian targets

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INTEGRATED COMPUTER-AIDED ENGINEERING 2016年第3期23卷 299-311页

作者： Lacabex, Borja Cuesta-Infante, Alfredo Montemayor, Antonio S. Pantrigo, Juan J. Univ Rey Juan Carlos Deparment Comp Sci & Stat Madrid Spain

In this work we present a tracking-by-detection system that works under two hard computational constraints: no parallelization and real-time performance. To this aim, we develop a lightweight method for object detection based on background subtraction, window masking and image density projections. The association of detections and trackers is carried out with the Hungarian algorithm on a loss matrix that is constructed according to position, size and appearance. In addition we include heuristics for detecting forming groups as well as incoming and outgoing pedestrians. Finally tracking is performed with particle filters. We test our system on different well-known benchmark datasets. Experimental results reveal that the proposed method is efficient and effective. Specifically, it obtains a processing rate of up to 22 frames per second on average when tracking up to 9 people.

关键词： People detection people tracking tracking-by-detection association algorithms particle filters

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